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Let's face it: we already operate in a digital world; those still transforming and digitising their enterprises are simply trying to catch up. And in this world, data-driven technologies – things like Artificial Intelligence (AI), Intelligent Automation and Cognitive Computing – will be the key differentiator.

Not surprisingly, most financial institutions are now thinking about how they can use data-driven technologies to get the upper hand in an increasingly competitive financial services environment. Consider that in a recent KPMG global survey of nearly 800 financial services decision-makers (Guardians of trust, KPMG International, 2018), 35 percent said they had already adopted AI and 44 percent said they were about to. Similarly, 45 percent said they were already using predictive models or machine learning, while 44 percent said they were planning to.

Yet, as machines, computers and algorithms start to play a greater role in the day-to-day decision-making of financial institutions, many CEOs and executives (not to mention regulators and boards) are starting to question whether they can really trust the insights and conclusions that are being generated by the machines.

The same survey found just 33 percent of financial institution decision-makers have a high level of trust in the way their organisation uses different types of analytics. More than one in five said they either have limited trust or, worse, active distrust, in their analytics. They are most worried about their ability to measure the effectiveness of their analytics. But they also seem concerned about their data sourcing, data preparation and data integration.

Clearly, this lack of trust is a fundamental challenge for financial institutions fighting to move into the digital age. Decision-makers know that all of this new technology will ultimately lead to better decisions, better customer experiences and better financial results. They also know that machine learning is necessary if they want to remain competitive, agile and lean. The only thing holding them back is trust.

My experience working with leading banks, insurers and asset managers suggests that – when it comes to data and analytics – most executives' trust issues boil down to three main trust gaps: trust in their data; trust in their analytics models; and trust in their interpretive capabilities. In this article, I've offered a few ideas on how to bridge those gaps.

Trust in data and analytics is important. But that's not the only kind of trust that will be needed in the digital age. Customers will need to trust that their data is being protected and managed securely and appropriately. Business leaders will need to trust that their third-party service providers, alliance partners and technology providers are also managing data securely and working towards a shared vision. Regulators will need to trust that financial services organisations have robust processes to ensure integrity of the underlying data and efficacy of managerial decisions.

Perhaps most of all, senior business leaders will need to trust that all of this transformation, disruption and displacement is in the best interests of the business, its customers and its employees and its shareholders.

Overcoming the three main data and analytics trust gaps

Five ideas for building trust in data

Improve your data processes and governance models.

Create a dedicated data integrity function.

Enhance (or adopt) data management standards.

Appoint and empower a Chief Data Officer

Remove data silos to improve data integrity and transparency.

Five ideas for building trust in models

Improve awareness and understanding of analytical models.

Integrate the governance of humans and the governance of machines.

Assign ownership over algorithms and analytics to the business.

Develop a mechanism for continuous review and assessment of model inputs and outputs.

Five ideas for building trust in talent/capabilities

Start training or hiring for future talent/capabilities today to improve integration and understanding.

Evaluate your operating environment to ensure the right talent/capabilities are working together.

Create and encourage an analytics culture and embrace innovation.

Involve your current employees, and bring them along on the journey.

As this edition of Frontiers in Finance clearly illustrates, today's financial services workforce is undergoing serious transformation. And that change is creating massive challenges and opportunities for financial services HR leaders. What is the right talent mix to drive growth for all stages of the transformation (a question we explore in Beyond tech talent)? What role should machines play in the decision-making process and reducing costs? And how do you successfully manage the transition from today's workforce to the workforce of tomorrow? (See The augmented workforce for another look at that question).

Clearly, there is much uncertainty about what the future holds and what kind of workforce will be required to make it a success. What we do know, however, is that those companies able to navigate through a data-driven environment and balance shareholder and employee interests will ultimately be the ones that thrive in this digital environment.

KPMG International Cooperative (“KPMG International”) is a Swiss entity. Member firms of the KPMG network of independent firms are affiliated with KPMG International. KPMG International provides no client services. No member firm has any authority to obligate or bind KPMG International or any other member firm vis-à-vis third parties, nor does KPMG International have any such authority to obligate or bind any member firm.